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SBMO/IEEE MTT-S International Conference on Microwave and Optoelectronics (IMOC)

作者： Uilian José Dreyer Erlon V. da Silva Wilian Oliveira Paulo H. R. Mazzo Kleiton de M. Sousa André Biffe Di Renzo Cicero Martelli Jean Carlos Cardozo da Silva Graduate Program in Energy Systems (PPGSE) ENGlE Brasil Energia Universidade Tecnologica Federal do Parana-UTFPR Curitiba Parana Brazil Graduate Program in Electrical and Computer Engineering (CPGEI)

The carbon brushes and slip rings of a hydrogen-erator are the main components guiding the excitation current from the bridge to the rotor windings. The brushes' temperature are crucial to infer their operational condition and, the generator status. This work presents the temperature measurement of six Fiber Bragg Gratings (FBG) sensors installed in a 370 MVA electric generator brushes. The results show the sensors' capacity to monitor the brushes' temperature in accordance with the current flowing through them. Together with the sensors, an Artificial Intelligence (AI) technique was applied to the measured temperature to detect anomalous events regarding the current supplied to the rotor windings. The optical sensors combined with the AI could detect five events of abnormal current behaviour. One is presented in detail in this paper. This sensing system can be further applied to online fault detection using the temperature measured by the FBGs as a brush condition indicator and a generator operation and maintenance tool.

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A Harmony search algorithm combined with differential operator applied to reliability-redundancy optimization

A Harmony search algorithm combined with differential operat...

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2009 IEEE International Conference on systems, Man and Cybernetics, SMC 2009

作者： Dos Santos Coelho, Leandro De Andrade Bernert, Diego Luis Mariani, Viviana Cocco Industrial and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil Department of Mechanical Engineering PPGEM Pontifical Catholic University of Paraná Imaculada Conceição 1155 80215-901 Curitiba Paraná Brazil

ISBN: (纸本)9781424427949

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called harmony search (HS) algorithm has emerged. HS was conceptualized using an analogy with music improvisation process where music players improvise the pitches of their instruments to obtain better harmony. This paper introduces a modified HS approach combined with an operator of differential evolution - a paradigm of evolutionary computation - to solve optimization problems in reliability engineering. In this context, an example of mixed integer programming in reliability-redundancy design of an over-speed protection system for a gas turbine is evaluated. In this application domain, HS was found to outperform the previously best-known solutions available. ©2009 IEEE.

关键词： Integer programming

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A multi-kingdom genetic barcoding system for precise clone isolation

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Nature Biotechnology 2025年 1-14页

作者： Soh Ishiguro Rina C. Sakata Ren Takimoto Rina Yogo Yusuke Kijima Samuel King Omar Bashth Nanami Masuyama Arman Adel Nika Shakiba Nozomu Yachie Kana Ishida Minori Ichiraku Takuya Yamamoto Yasuhiro Takashima Hideto Mori Mamoru Tanaka Hiromi Toyoshima Motoaki Seki Hiroyuki Aburatani Shoko Tarumoto Taro Tsujimura Ju Hee Oh Anne-Sophie Archambault Akihiko Kondo Ramon I. Klein Geltink Keiji Nishida Satoru Kuhara School of Biomedical Engineering Faculty of Applied Science and Faculty of Medicine The University of British Columbia Vancouver British Columbia Canada Institute for Advanced Biosciences Keio University Tsuruoka Japan Systems Biology Program Graduate School of Media and Governance Keio University Fujisawa Japan Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe) The University of Osaka Osaka Japan Research Center for Advanced Science and Technology The University of Tokyo Tokyo Japan Spiber Inc Tsuruoka Japan Center for iPS Cell Research and Application Kyoto University Kyoto Japan Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan BC Children’s Hospital Research Institute Department of Pathology and Laboratory Medicine The University of British Columbia Vancouver British Columbia Canada Engineering Biology Research Center Kobe University Kobe Japan Graduate School of Science Technology and Innovation Kobe University Kobe Japan Department of Chemical Science and Engineering Graduate School of Engineering Kobe University Kobe Japan Graduate School of Bioresource and Bioenvironmental Sciences Faculty of Agriculture Kyushu University Fukuoka Japan

Cell-tagging strategies with DNA barcodes have enabled the analysis of clone size dynamics and clone-restricted transcriptomic landscapes in heterogeneous populations. However, isolating a target clone that displays a specific phenotype from a complex population remains challenging. Here we present a multi-kingdom genetic barcoding system, CloneSelect, which enables a target cell clone to be triggered to express a reporter gene for isolation through barcode-specific CRISPR base editing. In CloneSelect, cells are first stably tagged with DNA barcodes and propagated so that their subpopulation can be subjected to a given experiment. A clone that shows a phenotype or genotype of interest at a given time can then be isolated from the initial or subsequent cell pools stored during the experiment using CRISPR base editing. CloneSelect is scalable and compatible with single-cell RNA sequencing. We demonstrate the versatility of CloneSelect in human embryonic kidney 293T cells, mouse embryonic stem cells, human pluripotent stem cells, yeast cells and bacterial cells.

关键词： Genes

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Fuzzy Markov predictor in electric load forecasting

Fuzzy Markov predictor in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Brazil Systems Engineering and Computer Science Program E28093 Graduate Programs in Engineering (COPPE) Federal University of Rio de Janeiro Brazil

We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor (FBP) that was modified from the naive Bayes classifier. For verifying the efficiency of the FMP's prediction, we compare it with the FBP, one fuzzy system and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.

关键词： Load forecasting Fuzzy systems Hidden Markov models Power engineering and energy Smoothing methods Niobium compounds systems engineering and theory Computer science Predictive models Electric variables measurement

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A Mixed Integer Linear programming for COVID-19 Related Medical Waste Reverse Logistics Network Design 22

A Mixed Integer Linear Programming for COVID-19 Related Medi...

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4th International Conference on Management Science and Industrial engineering, MSIE 2022

作者： Santos, Aira Ronalie P. Maghfiroh, Meilinda Fitriani Nur Sapiter, John Rayam Prasetyo, Yogi Tri Redi, Anak Agung Ngurah Perwira Persada, Satria Fadil Young, Michael Nayat Nadlifatin, Reny Ardiansyahmiraja, Bobby School of Industrial Engineering and Engineering Management Mapua University Manila Philippines Department of Industrial Engineering Universitas Islam Indonesia Yogyakarta Indonesia Industrial Engineering Department Binus Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia Faculty of Business and Economics Universitas Surabaya Surabaya Indonesia

ISBN: (纸本)9781450395816

Medical wastes bring probable hazards and public risks if not handled correctly, especially during the covid-19 pandemic. Waste management costs are rising due to continues devastation of covid-19 virus. Increased care and handling processes have been implemented to avoid further spreading the infection while ensuring the proper disposal. Given this, medical waste related to covid-19, including other wastes generated by medical facilities, requires delicate integration into the waste management process thru a reverse logistics network for cost-efficient collection, processing, and transport style. Using AMPL software as solving tool, this paper was designed to utilize a mixed-integer linear programming model of a reverse logistics network to improve medical waste management by identifying the total minimum cost of the waste disposal cycle from Waste generating facilities, treatment facilities, and to the disposal factory. © 2022 ACM.

关键词： COVID-19

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Microscopic Simulation Analysis of Mainstream Traffic Flow Control with Variable Speed Limits

Microscopic Simulation Analysis of Mainstream Traffic Flow C...

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International IEEE Conference on Intelligent Transportation systems

作者： Eduardo R. Muller Rodrigo C. Carlson Werner Kraus Markos Papageorgiou Post-graduate Program in Automation and Systems Engineering Federal University of Santa Catarina Department of Production Engineering and Managament Technical University of Crete

ISBN: (纸本)9781479929153

Mainstream Traffic Flow Control (MTFC) with Variable Speed Limits (VSL) is a freeway traffic control method which aims to maximize throughput by regulating the mainstream flow upstream from a bottleneck. Recent studies in a macroscopic simulator have shown optimal and feedback MTFC potential to improve traffic conditions. In this paper, local feedback MTFC is applied in microscopic simulation for an on-ramp merge bottleneck. Traffic behavior reveals important aspects that had not been previously captured in macroscopic simulation. Mainly, the more realistic VSL application at specific points instead of along an entire freeway section produces a slower traffic response to speed limit changes. In addition, the nonlinear speed limit-flow relation observed in the microscopic model is more pronounced than what was observed at the macroscopic level. After appropriate modifications in the control law significant improvements in traffic conditions were obtained.

关键词： Vehicles Acceleration Microscopy Traffic control Delays Detectors Modeling

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Fuzzy model and particle swarm optimization for nonlinear identification of a Chua's oscillator

Fuzzy model and particle swarm optimization for nonlinear id...

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2007 IEEE International Conference on Fuzzy systems, FUZZY

作者： Araujo, Ernesto Dos Santos Coelho, Leandro IEEE Cx.P. 515 12.227-010 Sao Jose dos Campos SP Brazil Production and Systems Engineering Graduate Program PPGEPS Pontifical Catholic University of Parana Imaculada Conceicao 1155 80.215-901 Curitiba PR Brazil

ISBN: (纸本)1424412102

The identification of a nonlinear system with chaotic behavior denoted Chua's oscillator by using fuzzy models intertwined with particle swarm optimization (PSO) method is presented. This hybrid approach is applied to experimental data generated by an inductorless Chua's circuit that consists of an electronic chaotic oscillator. Chua's circuit has been used as a test platform by various scientific and engineering communities related to the study of chaos due its great potencial in technological applications, for instance, telecommunications, cryptography and physics. Fuzzy set theory has been evolved as a powerful modeling tool that can cope with uncertainties and nonlinearities in modeling and identification procedures. The identification of a optimized fuzzy model Takagi-Sugeno (T-S) fuzzy model involves two primary tasks: parameter tuning and structure optimization. The premise part of production rules is optimized here by using the particle swarm optimization method. In turn, least mean squares technique is applied to the consequent part of a T-S fuzzy model. Results indicate PSO method and Least Mean Square technique succeeded in constructing a T-S fuzzy model when dealing with chaotic dynamics obtained through experimental data supplied by inductorless Chua's electronic circuit. © 2007 IEEE.

关键词： Fuzzy set theory

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Developing a conceptual framework for assessing printing companies operations sustainability pratices 22

Developing a conceptual framework for assessing printing com...

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22nd International Conference on Production Research, ICPR 2013

作者： Kai, Dalton Alexandre De Lima, Edson Pinheiro Gouvea Da Costa, Sergio E. Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Imaculada Conceicao St. 1155 Curitiba Parana Brazil Federal University of Technlogy-Parana Curitiba Brazil Brazilian National Service for Industrial Apprenticeship-Parana Curitiba Brazil

There is a worldwide concern with sustainable development and this has compelled organizations to modify their management systems and operations design in order to fit to this new environment. To have a sustainable business and operations means to be economically viable, to reduce or even not generate waste, to conserve energy and natural resources, to be safe to workers, communities and consumers, to improve quality of life, and to share value. It is noticed that Brazilian printing companies understand these demands and they are promoting changes in their strategy, business models and operations systems. The main goal of this research is to propose a conceptual framework for organizing sustainability practices in printing industry companies' operations. It is intended to answer the following research question, based on manufacturing operations analysis: Does the printing industry runs its operations in a sustainable way? Observing standards practices for economic, environmental and social sustainable aspects. It was conducted a literature review and also it was recovered secondary data from governmental institutes and agencies as well from printing companies associations. These elements helped in characterizing the context, structures and processes for designing, implementing and managing printing enterprise sustainable operations. The proposed framework intends to contain best practices for developing a sustainable operation and to be used for auditing and normative purposes. This will allow companies to adapt their operations models and systems in order to create a real sustainable enterprise.

关键词： Sustainable development

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Machine learning-based analysis on factors influencing blood heavy metal concentrations in the Korean CHildren's ENvironmental health Study (Ko-CHENS)

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Science of the Total Environment 2025年 978卷

作者： Jung, Seowoo Shah, Surabhi Oh, Jongmin Bang, Yoorim Lee, Ji Hyen Kim, Hwan-Cheol Jeong, Kyoung Sook Park, Huibyeol Lee, Eun-Kyung Hong, Yun-Chul Ha, Eunhee Department of Statistics Ewha Womans University Seoul Korea Republic of Department of Environmental Medicine College of Medicine Ewha Womans University Seoul Korea Republic of Department of Human Systems Medicine College of Medicine Seoul National University Seoul Korea Republic of College of Medicine Ewha Womans University Seoul Korea Republic of Institute for Development and Human Security Ewha Womans University Seoul Korea Republic of Department of Pediatrics College of Medicine Ewha Womans University Seoul Korea Republic of Department of Occupational and Environmental Medicine College of Medicine Inha University Hospital Inha University Incheon Korea Republic of Department of Occupational and Environmental Medicine Wonju College of Medicine Wonju Severance Christian Hospital Yonsei University Wonju Korea Republic of Environmental Health Research Division National Institute of Environmental Research Incheon Korea Republic of Institute of Environmental Medicine Medical Research Center Seoul National University Korea Republic of Graduate Program in System Health Science and Engineering Ewha Womans University Ewha Medical Research Institute College of Medicine Seoul Korea Republic of

Heavy metal concentration in pregnant women affects neurocognitive and behavioral development of their infants and children. The majority of existing research focusing on pregnant women's heavy metal concentration has considered individual environmental factor. In this study, we aim to comprehensively consider lifestyle, food, and environmental factors to determine the most influential factor affecting heavy metal concentration in pregnant women. The Ko-CHENS (Korean CHildren health and ENvironmental Study) is a nationwide prospective birth cohort study in South Korea enrolling pregnant women from 2015 to 2020. A total of 5458 eligible pregnant women were included in this study, and 897 variables were included in questionnaire comprising: maternal general information, indoor and living environment, dietary habits, health behavior, exposure to chemicals. Lead, cadmium and mercury concentration on blood were measured in early, late pregnancy and in cord blood at birth. Variables that might be related to heavy metal concentrations were included in machine learning models. Random forest and XGBoost machine learning models were conducted for predictions. Both models had similar but better performance than multiple linear regression. Kimchi (β = 1.55), seaweed (β = 0.40), fatty fish (β = 1.55), intakes respectively affected lead, cadmium, and mercury exposure through early, late pregnancy and cord blood. © 2025

关键词： Obstetrics

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